






"""simple docstring"""





import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
  import torch

  from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class 	a		:






    def __init__(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				str=None			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=None			,		__SCREAMING_SNAKE_CASE	:				str=None			,		__SCREAMING_SNAKE_CASE	:				List[str]="resnet50"			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=3			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]=32			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=3			,		__SCREAMING_SNAKE_CASE	:				int=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=True			,		)	->						Dict:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		out_indices if out_indices is not None else [4]
      lowerCamelCase_       	=		stage_names
      lowerCamelCase_       	=		out_features
      lowerCamelCase_       	=		backbone
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		use_pretrained_backbone
      lowerCamelCase_       	=		is_training






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values






    def   UpperCamelCase			(			self	:				str   )	->						List[Any]:
      return TimmBackboneConfig(
          image_size=self.image_size			,		num_channels=self.num_channels			,		out_features=self.out_features			,		out_indices=self.out_indices			,		stage_names=self.stage_names			,		use_pretrained_backbone=self.use_pretrained_backbone			,		backbone=self.backbone			,		)






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]   )	->						List[str]:
      lowerCamelCase_       	=		TimmBackbone(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      with torch.no_grad():
        lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(
          result.feature_map[-1].shape			,		(self.batch_size, model.channels[-1], 14, 14)			,		)






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Any:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
@require_timm
class 	a		(   __snake_case    , __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	List[Any]		=					(TimmBackbone,) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	List[Any]		=					{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
    SCREAMING_SNAKE_CASE		:	List[str]		=					False
    SCREAMING_SNAKE_CASE		:	Dict		=					False
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					False
    SCREAMING_SNAKE_CASE		:	int		=					False






    def   UpperCamelCase			(			self	:				Dict   )	->						List[str]:
      lowerCamelCase_       	=		TimmBackboneModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Dict:
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[int]:
      lowerCamelCase_       	=		'resnet18'
      lowerCamelCase_       	=		'microsoft/resnet-18'

      lowerCamelCase_       	=		AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE			,		use_timm_backbone=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE   )

      self.assertEqual(len(timm_model.out_features   )			,		len(transformers_model.out_features   )   )
      self.assertEqual(len(timm_model.stage_names   )			,		len(transformers_model.stage_names   )   )
      self.assertEqual(timm_model.channels			,		transformers_model.channels   )
      # Out indices are set to the last layer by default. For timm models, we don't know
      # the number of layers in advance, so we set it to (-1,), whereas for transformers
      # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
      self.assertEqual(timm_model.out_indices			,		(-1,)   )
      self.assertEqual(transformers_model.out_indices			,		[len(timm_model.stage_names   ) - 1]   )

      lowerCamelCase_       	=		AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE			,		use_timm_backbone=__SCREAMING_SNAKE_CASE			,		out_indices=[1, 2, 3]   )
      lowerCamelCase_       	=		AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE			,		out_indices=[1, 2, 3]   )

      self.assertEqual(timm_model.out_indices			,		transformers_model.out_indices   )
      self.assertEqual(len(timm_model.out_features   )			,		len(transformers_model.out_features   )   )
      self.assertEqual(timm_model.channels			,		transformers_model.channels   )






    @unittest.skip('TimmBackbone doesn\'t support feed forward chunking'   )
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      pass






    @unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'   )
    def   UpperCamelCase			(			self	:				str   )	->						str:
      pass






    @unittest.skip('TimmBackbone initialization is managed on the timm side'   )
    def   UpperCamelCase			(			self	:				List[Any]   )	->						Any:
      pass






    @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'   )
    def   UpperCamelCase			(			self	:				List[str]   )	->						Union[str, Any]:
      pass






    @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'   )
    def   UpperCamelCase			(			self	:				List[str]   )	->						int:
      pass






    @unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'   )
    def   UpperCamelCase			(			self	:				Dict   )	->						int:
      pass






    @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'   )
    def   UpperCamelCase			(			self	:				int   )	->						int:
      pass






    @unittest.skip('model weights aren\'t tied in TimmBackbone.'   )
    def   UpperCamelCase			(			self	:				Dict   )	->						Optional[Any]:
      pass






    @unittest.skip('model weights aren\'t tied in TimmBackbone.'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						str:
      pass






    @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'   )
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      pass






    @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'   )
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      pass






    @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'   )
    def   UpperCamelCase			(			self	:				int   )	->						str:
      pass






    @unittest.skip('TimmBackbone doesn\'t support output_attentions.'   )
    def   UpperCamelCase			(			self	:				List[Any]   )	->						Tuple:
      pass






    @unittest.skip('Safetensors is not supported by timm.'   )
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Any:
      pass






    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
    def   UpperCamelCase			(			self	:				int   )	->						str:
      pass






    def   UpperCamelCase			(			self	:				str   )	->						Dict:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str   )	->						str:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()
      lowerCamelCase_       	=		True
      lowerCamelCase_       	=		self.has_attentions

      # no need to test all models as different heads yield the same functionality
      lowerCamelCase_       	=		self.all_model_classes[0]
      lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		outputs[0][-1]

      # Encoder-/Decoder-only models
      lowerCamelCase_       	=		outputs.hidden_states[0]
      hidden_states.retain_grad()

      if self.has_attentions:
        lowerCamelCase_       	=		outputs.attentions[0]
        attentions.retain_grad()

      output.flatten()[0].backward(retain_graph=__SCREAMING_SNAKE_CASE   )

      self.assertIsNotNone(hidden_states.grad   )

      if self.has_attentions:
        self.assertIsNotNone(attentions.grad   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						int:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

        self.assertEqual(len(result.feature_maps   )			,		len(config.out_indices   )   )
        self.assertEqual(len(model.channels   )			,		len(config.out_indices   )   )

        # Check output of last stage is taken if out_features=None, out_indices=None
        lowerCamelCase_       	=		copy.deepcopy(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		None
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

        self.assertEqual(len(result.feature_maps   )			,		1   )
        self.assertEqual(len(model.channels   )			,		1   )

        # Check backbone can be initialized with fresh weights
        lowerCamelCase_       	=		copy.deepcopy(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		False
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




